{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading and Processing Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "(5000, 3, 96, 96)\n"
     ]
    }
   ],
   "source": [
    "from torchvision import datasets\n",
    "import torchvision.transforms as transforms\n",
    "import os\n",
    "\n",
    "# path to store/load data\n",
    "path2data=\"./data\"\n",
    "if not os.path.exists(path2data):\n",
    "    os.mkdir(path2data)\n",
    "    \n",
    "# define transformation\n",
    "data_transformer = transforms.Compose([transforms.ToTensor()])\n",
    "    \n",
    "# loading data\n",
    "train_ds=datasets.STL10(path2data, split='train', download=True,transform=data_transformer)\n",
    "\n",
    "# print out data shape\n",
    "print(train_ds.data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({1: 500, 5: 500, 6: 500, 3: 500, 9: 500, 7: 500, 4: 500, 8: 500, 0: 500, 2: 500})\n"
     ]
    }
   ],
   "source": [
    "import collections\n",
    "\n",
    "# get labels\n",
    "y_train=[y for _,y in train_ds]\n",
    "\n",
    "# count labels\n",
    "counter_train=collections.Counter(y_train)\n",
    "print(counter_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "(8000, 3, 96, 96)\n"
     ]
    }
   ],
   "source": [
    "# loading data\n",
    "test0_ds=datasets.STL10(path2data, split='test', download=True,transform=data_transformer)\n",
    "print(test0_ds.data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test: [2096 4321 2767 ... 3206 3910 2902] val: [6332 6852 1532 ... 5766 4469 1011]\n",
      "1600 6400\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "\n",
    "sss = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=0)\n",
    "\n",
    "indices=list(range(len(test0_ds)))\n",
    "y_test0=[y for _,y in test0_ds]\n",
    "for test_index, val_index in sss.split(indices, y_test0):\n",
    "    print(\"test:\", test_index, \"val:\", val_index)\n",
    "    print(len(val_index),len(test_index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import Subset\n",
    "\n",
    "val_ds=Subset(test0_ds,val_index)\n",
    "test_ds=Subset(test0_ds,test_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({6: 640, 0: 640, 4: 640, 5: 640, 9: 640, 2: 640, 3: 640, 1: 640, 7: 640, 8: 640})\n",
      "Counter({2: 160, 8: 160, 3: 160, 6: 160, 4: 160, 1: 160, 5: 160, 9: 160, 0: 160, 7: 160})\n"
     ]
    }
   ],
   "source": [
    "import collections\n",
    "import numpy as np\n",
    "\n",
    "# get labels\n",
    "y_test=[y for _,y in test_ds]\n",
    "y_val=[y for _,y in val_ds]\n",
    "\n",
    "counter_test=collections.Counter(y_test)\n",
    "counter_val=collections.Counter(y_val)\n",
    "\n",
    "print(counter_test)\n",
    "print(counter_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image indices: [2732 2607 1653 3264]\n",
      "torch.Size([3, 100, 394])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from torchvision import utils\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "\n",
    "# fix random seed\n",
    "np.random.seed(0)\n",
    "\n",
    "def show(img,y=None,color=True):\n",
    "    npimg = img.numpy()\n",
    "    npimg_tr=np.transpose(npimg, (1,2,0))\n",
    "    plt.imshow(npimg_tr)\n",
    "    if y is not None:\n",
    "        plt.title(\"label: \"+str(y))\n",
    "        \n",
    "grid_size=4\n",
    "rnd_inds=np.random.randint(0,len(train_ds),grid_size)\n",
    "print(\"image indices:\",rnd_inds)\n",
    "\n",
    "x_grid=[train_ds[i][0] for i in rnd_inds]\n",
    "y_grid=[train_ds[i][1] for i in rnd_inds]\n",
    "\n",
    "x_grid=utils.make_grid(x_grid, nrow=4, padding=2)\n",
    "print(x_grid.shape)\n",
    "\n",
    "# call helper function\n",
    "plt.figure(figsize=(10,10))\n",
    "show(x_grid,y_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image indices: [ 684  559 1216  835]\n",
      "torch.Size([3, 100, 394])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(0)\n",
    "\n",
    "grid_size=4\n",
    "rnd_inds=np.random.randint(0,len(val_ds),grid_size)\n",
    "print(\"image indices:\",rnd_inds)\n",
    "\n",
    "x_grid=[val_ds[i][0] for i in rnd_inds]\n",
    "y_grid=[val_ds[i][1] for i in rnd_inds]\n",
    "\n",
    "x_grid=utils.make_grid(x_grid, nrow=4, padding=2)\n",
    "print(x_grid.shape)\n",
    "\n",
    "# call helper function\n",
    "plt.figure(figsize=(10,10))\n",
    "show(x_grid,y_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4467106 0.43980986 0.40664646\n",
      "0.22414584 0.22148906 0.22389975\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# RGB mean and std \n",
    "meanRGB=[np.mean(x.numpy(),axis=(1,2)) for x,_ in train_ds]\n",
    "stdRGB=[np.std(x.numpy(),axis=(1,2)) for x,_ in train_ds]\n",
    "\n",
    "meanR=np.mean([m[0] for m in meanRGB])\n",
    "meanG=np.mean([m[1] for m in meanRGB])\n",
    "meanB=np.mean([m[2] for m in meanRGB])\n",
    "\n",
    "stdR=np.mean([s[0] for s in stdRGB])\n",
    "stdG=np.mean([s[1] for s in stdRGB])\n",
    "stdB=np.mean([s[2] for s in stdRGB])\n",
    "\n",
    "print(meanR,meanG,meanB)\n",
    "print(stdR,stdG,stdB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_transformer = transforms.Compose([\n",
    "    transforms.RandomHorizontalFlip(p=0.5),  \n",
    "    transforms.RandomVerticalFlip(p=0.5),  \n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize([meanR, meanG, meanB], [stdR, stdG, stdB])])\n",
    "                 \n",
    "\n",
    "test0_transformer = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize([meanR, meanG, meanB], [stdR, stdG, stdB]),\n",
    "    ])   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# overwrite the transform functions\n",
    "train_ds.transform=train_transformer\n",
    "test0_ds.transform=test0_transformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image indices: [2732 2607 1653 3264]\n",
      "torch.Size([3, 100, 394])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "np.random.seed(0)\n",
    "torch.manual_seed(0)\n",
    "\n",
    "# make a grid\n",
    "grid_size=4\n",
    "rnd_inds=np.random.randint(0,len(train_ds),grid_size)\n",
    "print(\"image indices:\",rnd_inds)\n",
    "\n",
    "x_grid=[train_ds[i][0] for i in rnd_inds]\n",
    "y_grid=[train_ds[i][1] for i in rnd_inds]\n",
    "\n",
    "x_grid=utils.make_grid(x_grid, nrow=4, padding=2)\n",
    "print(x_grid.shape)\n",
    "\n",
    "# call helper function\n",
    "plt.figure(figsize=(10,10))\n",
    "show(x_grid,y_grid)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "\n",
    "train_dl = DataLoader(train_ds, batch_size=32, shuffle=True)\n",
    "val_dl = DataLoader(val_ds, batch_size=64, shuffle=False)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32, 3, 96, 96])\n",
      "torch.Size([32])\n"
     ]
    }
   ],
   "source": [
    "# extract a batch from training data\n",
    "for x, y in train_dl:\n",
    "    print(x.shape)\n",
    "    print(y.shape)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([64, 3, 96, 96])\n",
      "torch.Size([64])\n"
     ]
    }
   ],
   "source": [
    "# extract a batch from validation data\n",
    "for x, y in val_dl:\n",
    "    print(x.shape)\n",
    "    print(y.shape)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "fashion_train=datasets.FashionMNIST(path2data, train=True, download=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchvision import models\n",
    "import torch\n",
    "\n",
    "# load model with random weights\n",
    "model_resnet18 = models.resnet18(pretrained=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ResNet(\n",
      "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
      "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (relu): ReLU(inplace)\n",
      "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
      "  (layer1): Sequential(\n",
      "    (0): BasicBlock(\n",
      "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace)\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    )\n",
      "    (1): BasicBlock(\n",
      "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace)\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    )\n",
      "  )\n",
      "  (layer2): Sequential(\n",
      "    (0): BasicBlock(\n",
      "      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): BasicBlock(\n",
      "      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    )\n",
      "  )\n",
      "  (layer3): Sequential(\n",
      "    (0): BasicBlock(\n",
      "      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): BasicBlock(\n",
      "      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    )\n",
      "  )\n",
      "  (layer4): Sequential(\n",
      "    (0): BasicBlock(\n",
      "      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace)\n",
      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): BasicBlock(\n",
      "      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace)\n",
      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    )\n",
      "  )\n",
      "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
      "  (fc): Linear(in_features=512, out_features=1000, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "print(model_resnet18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ResNet(\n",
       "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (relu): ReLU(inplace)\n",
       "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "  (layer1): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (layer2): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (layer3): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (layer4): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
       "  (fc): Linear(in_features=512, out_features=10, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from torch import nn\n",
    "# change the output layer\n",
    "num_classes=10\n",
    "num_ftrs = model_resnet18.fc.in_features \n",
    "model_resnet18.fc = nn.Linear(num_ftrs, num_classes)\n",
    "\n",
    "device = torch.device(\"cuda:0\")\n",
    "model_resnet18.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Conv2d-1         [-1, 64, 112, 112]           9,408\n",
      "       BatchNorm2d-2         [-1, 64, 112, 112]             128\n",
      "              ReLU-3         [-1, 64, 112, 112]               0\n",
      "         MaxPool2d-4           [-1, 64, 56, 56]               0\n",
      "            Conv2d-5           [-1, 64, 56, 56]          36,864\n",
      "       BatchNorm2d-6           [-1, 64, 56, 56]             128\n",
      "              ReLU-7           [-1, 64, 56, 56]               0\n",
      "            Conv2d-8           [-1, 64, 56, 56]          36,864\n",
      "       BatchNorm2d-9           [-1, 64, 56, 56]             128\n",
      "             ReLU-10           [-1, 64, 56, 56]               0\n",
      "       BasicBlock-11           [-1, 64, 56, 56]               0\n",
      "           Conv2d-12           [-1, 64, 56, 56]          36,864\n",
      "      BatchNorm2d-13           [-1, 64, 56, 56]             128\n",
      "             ReLU-14           [-1, 64, 56, 56]               0\n",
      "           Conv2d-15           [-1, 64, 56, 56]          36,864\n",
      "      BatchNorm2d-16           [-1, 64, 56, 56]             128\n",
      "             ReLU-17           [-1, 64, 56, 56]               0\n",
      "       BasicBlock-18           [-1, 64, 56, 56]               0\n",
      "           Conv2d-19          [-1, 128, 28, 28]          73,728\n",
      "      BatchNorm2d-20          [-1, 128, 28, 28]             256\n",
      "             ReLU-21          [-1, 128, 28, 28]               0\n",
      "           Conv2d-22          [-1, 128, 28, 28]         147,456\n",
      "      BatchNorm2d-23          [-1, 128, 28, 28]             256\n",
      "           Conv2d-24          [-1, 128, 28, 28]           8,192\n",
      "      BatchNorm2d-25          [-1, 128, 28, 28]             256\n",
      "             ReLU-26          [-1, 128, 28, 28]               0\n",
      "       BasicBlock-27          [-1, 128, 28, 28]               0\n",
      "           Conv2d-28          [-1, 128, 28, 28]         147,456\n",
      "      BatchNorm2d-29          [-1, 128, 28, 28]             256\n",
      "             ReLU-30          [-1, 128, 28, 28]               0\n",
      "           Conv2d-31          [-1, 128, 28, 28]         147,456\n",
      "      BatchNorm2d-32          [-1, 128, 28, 28]             256\n",
      "             ReLU-33          [-1, 128, 28, 28]               0\n",
      "       BasicBlock-34          [-1, 128, 28, 28]               0\n",
      "           Conv2d-35          [-1, 256, 14, 14]         294,912\n",
      "      BatchNorm2d-36          [-1, 256, 14, 14]             512\n",
      "             ReLU-37          [-1, 256, 14, 14]               0\n",
      "           Conv2d-38          [-1, 256, 14, 14]         589,824\n",
      "      BatchNorm2d-39          [-1, 256, 14, 14]             512\n",
      "           Conv2d-40          [-1, 256, 14, 14]          32,768\n",
      "      BatchNorm2d-41          [-1, 256, 14, 14]             512\n",
      "             ReLU-42          [-1, 256, 14, 14]               0\n",
      "       BasicBlock-43          [-1, 256, 14, 14]               0\n",
      "           Conv2d-44          [-1, 256, 14, 14]         589,824\n",
      "      BatchNorm2d-45          [-1, 256, 14, 14]             512\n",
      "             ReLU-46          [-1, 256, 14, 14]               0\n",
      "           Conv2d-47          [-1, 256, 14, 14]         589,824\n",
      "      BatchNorm2d-48          [-1, 256, 14, 14]             512\n",
      "             ReLU-49          [-1, 256, 14, 14]               0\n",
      "       BasicBlock-50          [-1, 256, 14, 14]               0\n",
      "           Conv2d-51            [-1, 512, 7, 7]       1,179,648\n",
      "      BatchNorm2d-52            [-1, 512, 7, 7]           1,024\n",
      "             ReLU-53            [-1, 512, 7, 7]               0\n",
      "           Conv2d-54            [-1, 512, 7, 7]       2,359,296\n",
      "      BatchNorm2d-55            [-1, 512, 7, 7]           1,024\n",
      "           Conv2d-56            [-1, 512, 7, 7]         131,072\n",
      "      BatchNorm2d-57            [-1, 512, 7, 7]           1,024\n",
      "             ReLU-58            [-1, 512, 7, 7]               0\n",
      "       BasicBlock-59            [-1, 512, 7, 7]               0\n",
      "           Conv2d-60            [-1, 512, 7, 7]       2,359,296\n",
      "      BatchNorm2d-61            [-1, 512, 7, 7]           1,024\n",
      "             ReLU-62            [-1, 512, 7, 7]               0\n",
      "           Conv2d-63            [-1, 512, 7, 7]       2,359,296\n",
      "      BatchNorm2d-64            [-1, 512, 7, 7]           1,024\n",
      "             ReLU-65            [-1, 512, 7, 7]               0\n",
      "       BasicBlock-66            [-1, 512, 7, 7]               0\n",
      "AdaptiveAvgPool2d-67            [-1, 512, 1, 1]               0\n",
      "           Linear-68                   [-1, 10]           5,130\n",
      "================================================================\n",
      "Total params: 11,181,642\n",
      "Trainable params: 11,181,642\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 0.57\n",
      "Forward/backward pass size (MB): 62.79\n",
      "Params size (MB): 42.65\n",
      "Estimated Total Size (MB): 106.01\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from torchsummary import summary\n",
    "summary(model_resnet18, input_size=(3, 224, 224))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([64, 3, 7, 7])\n",
      "0.0 1.0835012197494507\n",
      "torch.Size([3, 65, 65])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get Conv1 weights\n",
    "for w in model_resnet18.parameters():\n",
    "    w=w.data.cpu()\n",
    "    print(w.shape)\n",
    "    break\n",
    "\n",
    "# normalize to [0,1]\n",
    "min_w=torch.min(w)\n",
    "w1 = (-1/(2*min_w))*w + 0.5 \n",
    "print(torch.min(w1).item(),torch.max(w1).item())\n",
    "\n",
    "# make a grid\n",
    "grid_size=len(w1)\n",
    "x_grid=[w1[i] for i in range(grid_size)]\n",
    "x_grid=utils.make_grid(x_grid, nrow=8, padding=1)\n",
    "print(x_grid.shape)\n",
    "\n",
    "# call helper function\n",
    "plt.figure(figsize=(5,5))\n",
    "show(x_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ResNet(\n",
       "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (relu): ReLU(inplace)\n",
       "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "  (layer1): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (layer2): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (layer3): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (layer4): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
       "  (fc): Linear(in_features=512, out_features=10, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from torchvision import models\n",
    "import torch\n",
    "\n",
    "# load model with pretrained weights\n",
    "resnet18_pretrained = models.resnet18(pretrained=True)\n",
    "\n",
    "# change the output layer\n",
    "num_classes=10\n",
    "num_ftrs = resnet18_pretrained.fc.in_features\n",
    "resnet18_pretrained.fc = nn.Linear(num_ftrs, num_classes)\n",
    "\n",
    "device = torch.device(\"cuda:0\")\n",
    "resnet18_pretrained.to(device) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([64, 3, 7, 7])\n",
      "0.0 1.102618932723999\n",
      "torch.Size([3, 65, 65])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get Conv1 weights\n",
    "for w in resnet18_pretrained.parameters():\n",
    "    w=w.data.cpu()\n",
    "    print(w.shape)\n",
    "    break\n",
    "\n",
    "# normalize to [0,1]\n",
    "min_w=torch.min(w)\n",
    "w1 = (-1/(2*min_w))*w + 0.5 \n",
    "print(torch.min(w1).item(),torch.max(w1).item())\n",
    "\n",
    "# make a grid\n",
    "grid_size=len(w1)\n",
    "x_grid=[w1[i] for i in range(grid_size)]\n",
    "x_grid=utils.make_grid(x_grid, nrow=8, padding=1)\n",
    "print(x_grid.shape)\n",
    "\n",
    "# call helper function\n",
    "plt.figure(figsize=(5,5))\n",
    "show(x_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VGG(\n",
      "  (features): Sequential(\n",
      "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (1): ReLU(inplace)\n",
      "    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (3): ReLU(inplace)\n",
      "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (6): ReLU(inplace)\n",
      "    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (8): ReLU(inplace)\n",
      "    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (11): ReLU(inplace)\n",
      "    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (13): ReLU(inplace)\n",
      "    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (15): ReLU(inplace)\n",
      "    (16): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (17): ReLU(inplace)\n",
      "    (18): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (19): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (20): ReLU(inplace)\n",
      "    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (22): ReLU(inplace)\n",
      "    (23): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (24): ReLU(inplace)\n",
      "    (25): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (26): ReLU(inplace)\n",
      "    (27): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (29): ReLU(inplace)\n",
      "    (30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (31): ReLU(inplace)\n",
      "    (32): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (33): ReLU(inplace)\n",
      "    (34): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (35): ReLU(inplace)\n",
      "    (36): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))\n",
      "  (classifier): Sequential(\n",
      "    (0): Linear(in_features=25088, out_features=4096, bias=True)\n",
      "    (1): ReLU(inplace)\n",
      "    (2): Dropout(p=0.5)\n",
      "    (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
      "    (4): ReLU(inplace)\n",
      "    (5): Dropout(p=0.5)\n",
      "    (6): Linear(in_features=4096, out_features=10, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "num_classes=10\n",
    "vgg19 = models.vgg19(pretrained=True)\n",
    "# change the last layer\n",
    "vgg19.classifier[6] = nn.Linear(4096,num_classes)\n",
    "print(vgg19)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define Loss Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss_func = nn.CrossEntropyLoss(reduction=\"sum\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([4, 5])\n",
      "torch.Size([4])\n",
      "7.312585830688477\n"
     ]
    }
   ],
   "source": [
    "# fix random seed\n",
    "torch.manual_seed(0)\n",
    "\n",
    "n,c=4,5\n",
    "y = torch.randn(n, c, requires_grad=True)\n",
    "print(y.shape)\n",
    "\n",
    "loss_func = nn.CrossEntropyLoss(reduction=\"sum\")\n",
    "target = torch.randint(c,size=(n,))\n",
    "print(target.shape)\n",
    "\n",
    "loss = loss_func(y, target)\n",
    "print(loss.item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[-1.1258, -1.1524, -0.2506, -0.4339,  0.5988],\n",
      "        [-1.5551, -0.3414,  1.8530,  0.4681, -0.1577],\n",
      "        [ 1.4437,  0.2660,  1.3894,  1.5863,  0.9463],\n",
      "        [-0.8437,  0.9318,  1.2590,  2.0050,  0.0537]])\n"
     ]
    }
   ],
   "source": [
    "loss.backward()\n",
    "print (y.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Defining Optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch import optim\n",
    "opt = optim.Adam(model_resnet18.parameters(), lr=1e-4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "current lr=0.0001\n"
     ]
    }
   ],
   "source": [
    "# get learning rate \n",
    "def get_lr(opt):\n",
    "    for param_group in opt.param_groups:\n",
    "        return param_group['lr']\n",
    "\n",
    "current_lr=get_lr(opt)\n",
    "print('current lr={}'.format(current_lr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.optim.lr_scheduler import CosineAnnealingLR\n",
    "\n",
    "# define learning rate scheduler\n",
    "lr_scheduler = CosineAnnealingLR(opt,T_max=2,eta_min=1e-5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 0, lr: 1.0e-04\n",
      "epoch 1, lr: 5.5e-05\n",
      "epoch 2, lr: 1.0e-05\n",
      "epoch 3, lr: 5.5e-05\n",
      "epoch 4, lr: 1.0e-04\n",
      "epoch 5, lr: 5.5e-05\n",
      "epoch 6, lr: 1.0e-05\n",
      "epoch 7, lr: 5.5e-05\n",
      "epoch 8, lr: 1.0e-04\n",
      "epoch 9, lr: 5.5e-05\n"
     ]
    }
   ],
   "source": [
    "lrs=[]\n",
    "for i in range(10):\n",
    "    lr_scheduler.step()\n",
    "    lr=get_lr(opt)\n",
    "    print(\"epoch %s, lr: %.1e\" %(i,lr))\n",
    "    lrs.append(lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f0f3962c048>]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(lrs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training and Transfer Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def metrics_batch(output, target):\n",
    "    # get output class\n",
    "    pred = output.argmax(dim=1, keepdim=True)\n",
    "    \n",
    "    # compare output class with target class\n",
    "    corrects=pred.eq(target.view_as(pred)).sum().item()\n",
    "    return corrects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loss_batch(loss_func, output, target, opt=None):\n",
    "    \n",
    "    # get loss \n",
    "    loss = loss_func(output, target)\n",
    "    \n",
    "    # get performance metric\n",
    "    metric_b = metrics_batch(output,target)\n",
    "    \n",
    "    if opt is not None:\n",
    "        opt.zero_grad()\n",
    "        loss.backward()\n",
    "        opt.step()\n",
    "\n",
    "    return loss.item(), metric_b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define device as a global variable\n",
    "device = torch.device(\"cuda\")\n",
    "\n",
    "def loss_epoch(model,loss_func,dataset_dl,sanity_check=False,opt=None):\n",
    "    running_loss=0.0\n",
    "    running_metric=0.0\n",
    "    len_data=len(dataset_dl.dataset)\n",
    "\n",
    "    for xb, yb in dataset_dl:\n",
    "        # move batch to device\n",
    "        xb=xb.to(device)\n",
    "        yb=yb.to(device)\n",
    "        \n",
    "        # get model output\n",
    "        output=model(xb)\n",
    "        \n",
    "        # get loss per batch\n",
    "        loss_b,metric_b=loss_batch(loss_func, output, yb, opt)\n",
    "        \n",
    "        # update running loss\n",
    "        running_loss+=loss_b\n",
    "        \n",
    "        # update running metric\n",
    "        if metric_b is not None:\n",
    "            running_metric+=metric_b\n",
    "\n",
    "        # break the loop in case of sanity check\n",
    "        if sanity_check is True:\n",
    "            break\n",
    "    \n",
    "    # average loss value\n",
    "    loss=running_loss/float(len_data)\n",
    "    \n",
    "    # average metric value\n",
    "    metric=running_metric/float(len_data)\n",
    "    \n",
    "    return loss, metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_val(model, params):\n",
    "    # extract model parameters\n",
    "    num_epochs=params[\"num_epochs\"]\n",
    "    loss_func=params[\"loss_func\"]\n",
    "    opt=params[\"optimizer\"]\n",
    "    train_dl=params[\"train_dl\"]\n",
    "    val_dl=params[\"val_dl\"]\n",
    "    sanity_check=params[\"sanity_check\"]\n",
    "    lr_scheduler=params[\"lr_scheduler\"]\n",
    "    path2weights=params[\"path2weights\"]\n",
    "    \n",
    "    # history of loss values in each epoch\n",
    "    loss_history={\n",
    "        \"train\": [],\n",
    "        \"val\": [],\n",
    "    }\n",
    "    \n",
    "    # histroy of metric values in each epoch\n",
    "    metric_history={\n",
    "        \"train\": [],\n",
    "        \"val\": [],\n",
    "    }\n",
    "    \n",
    "    # a deep copy of weights for the best performing model\n",
    "    best_model_wts = copy.deepcopy(model.state_dict())\n",
    "    \n",
    "    # initialize best loss to a large value\n",
    "    best_loss=float('inf')\n",
    "    \n",
    "    # main loop\n",
    "    for epoch in range(num_epochs):\n",
    "        \n",
    "        # get current learning rate\n",
    "        current_lr=get_lr(opt)\n",
    "        print('Epoch {}/{}, current lr={}'.format(epoch, num_epochs - 1, current_lr))\n",
    "        \n",
    "        # train model on training dataset\n",
    "        model.train()\n",
    "        train_loss, train_metric=loss_epoch(model,loss_func,train_dl,sanity_check,opt)\n",
    "\n",
    "        # collect loss and metric for training dataset\n",
    "        loss_history[\"train\"].append(train_loss)\n",
    "        metric_history[\"train\"].append(train_metric)\n",
    "        \n",
    "        # evaluate model on validation dataset    \n",
    "        model.eval()\n",
    "        with torch.no_grad():\n",
    "            val_loss, val_metric=loss_epoch(model,loss_func,val_dl,sanity_check)\n",
    "        \n",
    "       \n",
    "        # store best model\n",
    "        if val_loss < best_loss:\n",
    "            best_loss = val_loss\n",
    "            best_model_wts = copy.deepcopy(model.state_dict())\n",
    "            \n",
    "            # store weights into a local file\n",
    "            torch.save(model.state_dict(), path2weights)\n",
    "            print(\"Copied best model weights!\")\n",
    "        \n",
    "        # collect loss and metric for validation dataset\n",
    "        loss_history[\"val\"].append(val_loss)\n",
    "        metric_history[\"val\"].append(val_metric)\n",
    "        \n",
    "        # learning rate schedule\n",
    "        lr_scheduler.step()\n",
    "\n",
    "        print(\"train loss: %.6f, dev loss: %.6f, accuracy: %.2f\" %(train_loss,val_loss,100*val_metric))\n",
    "        print(\"-\"*10) \n",
    "\n",
    "    # load best model weights\n",
    "    model.load_state_dict(best_model_wts)\n",
    "        \n",
    "    return model, loss_history, metric_history"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train With Random-Init Weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/2, current lr=0.0001\n",
      "Copied best model weights!\n",
      "train loss: 1.772097, dev loss: 1.615586, accuracy: 38.06\n",
      "----------\n",
      "Epoch 1/2, current lr=0.0001\n",
      "Copied best model weights!\n",
      "train loss: 1.429164, dev loss: 1.522995, accuracy: 44.25\n",
      "----------\n",
      "Epoch 2/2, current lr=9.05463412215599e-05\n",
      "Copied best model weights!\n",
      "train loss: 1.258205, dev loss: 1.442116, accuracy: 48.25\n",
      "----------\n"
     ]
    }
   ],
   "source": [
    "import copy\n",
    "\n",
    "loss_func = nn.CrossEntropyLoss(reduction=\"sum\")\n",
    "opt = optim.Adam(model_resnet18.parameters(), lr=1e-4)\n",
    "lr_scheduler = CosineAnnealingLR(opt,T_max=5,eta_min=1e-6)\n",
    "\n",
    "params_train={\n",
    " \"num_epochs\": 3,\n",
    " \"optimizer\": opt,\n",
    " \"loss_func\": loss_func,\n",
    " \"train_dl\": train_dl,\n",
    " \"val_dl\": val_dl,\n",
    " \"sanity_check\": False,\n",
    " \"lr_scheduler\": lr_scheduler,\n",
    " \"path2weights\": \"./models/resnet18.pt\",\n",
    "}\n",
    "\n",
    "# train and validate the model\n",
    "model_resnet18,loss_hist,metric_hist=train_val(model_resnet18,params_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train-Validation Progress\n",
    "num_epochs=params_train[\"num_epochs\"]\n",
    "\n",
    "# plot loss progress\n",
    "plt.title(\"Train-Val Loss\")\n",
    "plt.plot(range(1,num_epochs+1),loss_hist[\"train\"],label=\"train\")\n",
    "plt.plot(range(1,num_epochs+1),loss_hist[\"val\"],label=\"val\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.xlabel(\"Training Epochs\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# plot accuracy progress\n",
    "plt.title(\"Train-Val Accuracy\")\n",
    "plt.plot(range(1,num_epochs+1),metric_hist[\"train\"],label=\"train\")\n",
    "plt.plot(range(1,num_epochs+1),metric_hist[\"val\"],label=\"val\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.xlabel(\"Training Epochs\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train With Pre-Trained Weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/2, current lr=0.0001\n",
      "Copied best model weights!\n",
      "train loss: 0.894398, dev loss: 0.424265, accuracy: 85.38\n",
      "----------\n",
      "Epoch 1/2, current lr=0.0001\n",
      "train loss: 0.437394, dev loss: 0.436810, accuracy: 85.50\n",
      "----------\n",
      "Epoch 2/2, current lr=9.05463412215599e-05\n",
      "Copied best model weights!\n",
      "train loss: 0.312230, dev loss: 0.379482, accuracy: 87.81\n",
      "----------\n"
     ]
    }
   ],
   "source": [
    "import copy\n",
    "\n",
    "loss_func = nn.CrossEntropyLoss(reduction=\"sum\")\n",
    "opt = optim.Adam(resnet18_pretrained.parameters(), lr=1e-4)\n",
    "lr_scheduler = CosineAnnealingLR(opt,T_max=5,eta_min=1e-6)\n",
    "\n",
    "params_train={\n",
    " \"num_epochs\": 3,\n",
    " \"optimizer\": opt,\n",
    " \"loss_func\": loss_func,\n",
    " \"train_dl\": train_dl,\n",
    " \"val_dl\": val_dl,\n",
    " \"sanity_check\": False,\n",
    " \"lr_scheduler\": lr_scheduler,\n",
    " \"path2weights\": \"./models/resnet18_pretrained.pt\",\n",
    "}\n",
    "\n",
    "# train and validate the model\n",
    "resnet18_pretrained,loss_hist,metric_hist=train_val(resnet18_pretrained,params_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train-Validation Progress\n",
    "num_epochs=params_train[\"num_epochs\"]\n",
    "\n",
    "# plot loss progress\n",
    "plt.title(\"Train-Val Loss\")\n",
    "plt.plot(range(1,num_epochs+1),loss_hist[\"train\"],label=\"train\")\n",
    "plt.plot(range(1,num_epochs+1),loss_hist[\"val\"],label=\"val\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.xlabel(\"Training Epochs\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# plot accuracy progress\n",
    "plt.title(\"Train-Val Accuracy\")\n",
    "plt.plot(range(1,num_epochs+1),metric_hist[\"train\"],label=\"train\")\n",
    "plt.plot(range(1,num_epochs+1),metric_hist[\"val\"],label=\"val\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.xlabel(\"Training Epochs\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
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